{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "964ccced",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "from shap_e.diffusion.sample import sample_latents\n",
    "from shap_e.diffusion.gaussian_diffusion import diffusion_from_config\n",
    "from shap_e.models.download import load_model, load_config\n",
    "from shap_e.util.notebooks import create_pan_cameras, decode_latent_images, gif_widget\n",
    "from shap_e.util.image_util import load_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8eed3a76",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d922637",
   "metadata": {},
   "outputs": [],
   "source": [
    "xm = load_model('transmitter', device=device)\n",
    "model = load_model('image300M', device=device)\n",
    "diffusion = diffusion_from_config(load_config('diffusion'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "53d329d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 4\n",
    "guidance_scale = 3.0\n",
    "\n",
    "# To get the best result, you should remove the background and show only the object of interest to the model.\n",
    "image = load_image(\"example_data/corgi.png\")\n",
    "\n",
    "latents = sample_latents(\n",
    "    batch_size=batch_size,\n",
    "    model=model,\n",
    "    diffusion=diffusion,\n",
    "    guidance_scale=guidance_scale,\n",
    "    model_kwargs=dict(images=[image] * batch_size),\n",
    "    progress=True,\n",
    "    clip_denoised=True,\n",
    "    use_fp16=True,\n",
    "    use_karras=True,\n",
    "    karras_steps=64,\n",
    "    sigma_min=1e-3,\n",
    "    sigma_max=160,\n",
    "    s_churn=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "633da2ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "render_mode = 'nerf' # you can change this to 'stf' for mesh rendering\n",
    "size = 64 # this is the size of the renders; higher values take longer to render.\n",
    "\n",
    "cameras = create_pan_cameras(size, device)\n",
    "for i, latent in enumerate(latents):\n",
    "    images = decode_latent_images(xm, latent, cameras, rendering_mode=render_mode)\n",
    "    display(gif_widget(images))"
   ]
  }
 ],
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